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<a href="#pub-methods">Public 成员函数</a> &#124;
<a href="#pri-types">Private 类型</a> &#124;
<a href="#pri-attribs">Private 属性</a> &#124;
<a href="classpcl_1_1cuda_1_1_multi_random_sample_consensus-members.html">所有成员列表</a>  </div>
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<div class="title">pcl::cuda::MultiRandomSampleConsensus&lt; Storage &gt; 模板类 参考</div>  </div>
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<p><b><a class="el" href="classpcl_1_1cuda_1_1_random_sample_consensus.html" title="RandomSampleConsensus represents an implementation of the RANSAC (RAndom SAmple Consensus) algorithm,...">RandomSampleConsensus</a></b> represents an implementation of the RANSAC (RAndom SAmple Consensus) algorithm, as described in: "Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography", Martin A. Fischler and Robert C. Bolles, Comm. Of the ACM 24: 381–395, June 1981.  
 <a href="classpcl_1_1cuda_1_1_multi_random_sample_consensus.html#details">更多...</a></p>

<p><code>#include &lt;<a class="el" href="multi__ransac_8h_source.html">multi_ransac.h</a>&gt;</code></p>
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类 pcl::cuda::MultiRandomSampleConsensus&lt; Storage &gt; 继承关系图:</div>
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<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
Public 成员函数</h2></td></tr>
<tr class="memitem:af3a795dc44e988143c2bedb97f864938"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1cuda_1_1_multi_random_sample_consensus.html#af3a795dc44e988143c2bedb97f864938">MultiRandomSampleConsensus</a> (const SampleConsensusModelPtr &amp;model)</td></tr>
<tr class="memdesc:af3a795dc44e988143c2bedb97f864938"><td class="mdescLeft">&#160;</td><td class="mdescRight">RANSAC (RAndom SAmple Consensus) main constructor  <a href="classpcl_1_1cuda_1_1_multi_random_sample_consensus.html#af3a795dc44e988143c2bedb97f864938">更多...</a><br /></td></tr>
<tr class="separator:af3a795dc44e988143c2bedb97f864938"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a2d30c49d773e4ac88bf50d5019edd09e"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1cuda_1_1_multi_random_sample_consensus.html#a2d30c49d773e4ac88bf50d5019edd09e">MultiRandomSampleConsensus</a> (const SampleConsensusModelPtr &amp;model, double threshold)</td></tr>
<tr class="memdesc:a2d30c49d773e4ac88bf50d5019edd09e"><td class="mdescLeft">&#160;</td><td class="mdescRight">RANSAC (RAndom SAmple Consensus) main constructor  <a href="classpcl_1_1cuda_1_1_multi_random_sample_consensus.html#a2d30c49d773e4ac88bf50d5019edd09e">更多...</a><br /></td></tr>
<tr class="separator:a2d30c49d773e4ac88bf50d5019edd09e"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ae85ed7f021732575132f9a4b30a73eb5"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1cuda_1_1_multi_random_sample_consensus.html#ae85ed7f021732575132f9a4b30a73eb5">computeModel</a> (int debug_verbosity_level=0)</td></tr>
<tr class="memdesc:ae85ed7f021732575132f9a4b30a73eb5"><td class="mdescLeft">&#160;</td><td class="mdescRight">Compute the actual model and find the inliers  <a href="classpcl_1_1cuda_1_1_multi_random_sample_consensus.html#ae85ed7f021732575132f9a4b30a73eb5">更多...</a><br /></td></tr>
<tr class="separator:ae85ed7f021732575132f9a4b30a73eb5"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a6373987e0ed5079557c4f2c106f0a070"><td class="memItemLeft" align="right" valign="top"><a id="a6373987e0ed5079557c4f2c106f0a070"></a>
void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1cuda_1_1_multi_random_sample_consensus.html#a6373987e0ed5079557c4f2c106f0a070">setMinimumCoverage</a> (float percent)</td></tr>
<tr class="memdesc:a6373987e0ed5079557c4f2c106f0a070"><td class="mdescLeft">&#160;</td><td class="mdescRight">how much (in percent) of the point cloud should be covered? If it is not possible to find enough planes, it will stop according to the regular ransac criteria <br /></td></tr>
<tr class="separator:a6373987e0ed5079557c4f2c106f0a070"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a1e82feb9d732943a581a75a6737c184d"><td class="memItemLeft" align="right" valign="top"><a id="a1e82feb9d732943a581a75a6737c184d"></a>
void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1cuda_1_1_multi_random_sample_consensus.html#a1e82feb9d732943a581a75a6737c184d">setMaximumBatches</a> (int max_batches)</td></tr>
<tr class="memdesc:a1e82feb9d732943a581a75a6737c184d"><td class="mdescLeft">&#160;</td><td class="mdescRight">Sets the maximum number of batches that should be processed. Every Batch computes up to iterations_per_batch_ models and verifies them. If planes with a sufficiently high total inlier count are found earlier, the actual number of batch runs might be lower. <br /></td></tr>
<tr class="separator:a1e82feb9d732943a581a75a6737c184d"><td class="memSeparator" colspan="2">&#160;</td></tr>
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void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1cuda_1_1_multi_random_sample_consensus.html#a79a1d7b8f189cbdc7021d9fd6aec0280">setIerationsPerBatch</a> (int iterations_per_batch)</td></tr>
<tr class="memdesc:a79a1d7b8f189cbdc7021d9fd6aec0280"><td class="mdescLeft">&#160;</td><td class="mdescRight">Sets the maximum number of batches that should be processed. Every Batch computes up to max_iterations_ models and verifies them. If planes with a sufficiently high total inlier count are found earlier, the actual number of batch runs might be lower. <br /></td></tr>
<tr class="separator:a79a1d7b8f189cbdc7021d9fd6aec0280"><td class="memSeparator" colspan="2">&#160;</td></tr>
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std::vector&lt; IndicesPtr &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>getAllInliers</b> ()</td></tr>
<tr class="separator:aaa8b41559ed1e7728b3cc10dc2203d03"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a877107ffdd787f42b8a6e2a7125d3c66"><td class="memItemLeft" align="right" valign="top"><a id="a877107ffdd787f42b8a6e2a7125d3c66"></a>
std::vector&lt; int &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>getAllInlierCounts</b> ()</td></tr>
<tr class="separator:a877107ffdd787f42b8a6e2a7125d3c66"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ab55b29e094fb6a4eb34c50b0af5c892e"><td class="memItemLeft" align="right" valign="top"><a id="ab55b29e094fb6a4eb34c50b0af5c892e"></a>
std::vector&lt; float4 &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1cuda_1_1_multi_random_sample_consensus.html#ab55b29e094fb6a4eb34c50b0af5c892e">getAllModelCoefficients</a> ()</td></tr>
<tr class="memdesc:ab55b29e094fb6a4eb34c50b0af5c892e"><td class="mdescLeft">&#160;</td><td class="mdescRight">Return the model coefficients of the best model found so far. <br /></td></tr>
<tr class="separator:ab55b29e094fb6a4eb34c50b0af5c892e"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:adb8c9c810673d6c581b17963ca015d2c"><td class="memItemLeft" align="right" valign="top"><a id="adb8c9c810673d6c581b17963ca015d2c"></a>
std::vector&lt; float3 &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1cuda_1_1_multi_random_sample_consensus.html#adb8c9c810673d6c581b17963ca015d2c">getAllModelCentroids</a> ()</td></tr>
<tr class="memdesc:adb8c9c810673d6c581b17963ca015d2c"><td class="mdescLeft">&#160;</td><td class="mdescRight">Return the model coefficients of the best model found so far. <br /></td></tr>
<tr class="separator:adb8c9c810673d6c581b17963ca015d2c"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="inherit_header pub_methods_classpcl_1_1cuda_1_1_sample_consensus"><td colspan="2" onclick="javascript:toggleInherit('pub_methods_classpcl_1_1cuda_1_1_sample_consensus')"><img src="closed.png" alt="-"/>&#160;Public 成员函数 继承自 <a class="el" href="classpcl_1_1cuda_1_1_sample_consensus.html">pcl::cuda::SampleConsensus&lt; Storage &gt;</a></td></tr>
<tr class="memitem:a7bf4c6fd2f22b0c47d3e84991ea052df inherit pub_methods_classpcl_1_1cuda_1_1_sample_consensus"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1cuda_1_1_sample_consensus.html#a7bf4c6fd2f22b0c47d3e84991ea052df">SampleConsensus</a> (const SampleConsensusModelPtr &amp;model)</td></tr>
<tr class="memdesc:a7bf4c6fd2f22b0c47d3e84991ea052df inherit pub_methods_classpcl_1_1cuda_1_1_sample_consensus"><td class="mdescLeft">&#160;</td><td class="mdescRight">Constructor for base SAC.  <a href="classpcl_1_1cuda_1_1_sample_consensus.html#a7bf4c6fd2f22b0c47d3e84991ea052df">更多...</a><br /></td></tr>
<tr class="separator:a7bf4c6fd2f22b0c47d3e84991ea052df inherit pub_methods_classpcl_1_1cuda_1_1_sample_consensus"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a091bb1e129688f88e261a1dc940eb4a4 inherit pub_methods_classpcl_1_1cuda_1_1_sample_consensus"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1cuda_1_1_sample_consensus.html#a091bb1e129688f88e261a1dc940eb4a4">SampleConsensus</a> (const SampleConsensusModelPtr &amp;model, float threshold)</td></tr>
<tr class="memdesc:a091bb1e129688f88e261a1dc940eb4a4 inherit pub_methods_classpcl_1_1cuda_1_1_sample_consensus"><td class="mdescLeft">&#160;</td><td class="mdescRight">Constructor for base SAC.  <a href="classpcl_1_1cuda_1_1_sample_consensus.html#a091bb1e129688f88e261a1dc940eb4a4">更多...</a><br /></td></tr>
<tr class="separator:a091bb1e129688f88e261a1dc940eb4a4 inherit pub_methods_classpcl_1_1cuda_1_1_sample_consensus"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:acbeb087da86ee77a1d32cf9210a0f1e2 inherit pub_methods_classpcl_1_1cuda_1_1_sample_consensus"><td class="memItemLeft" align="right" valign="top"><a id="acbeb087da86ee77a1d32cf9210a0f1e2"></a>
virtual&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1cuda_1_1_sample_consensus.html#acbeb087da86ee77a1d32cf9210a0f1e2">~SampleConsensus</a> ()</td></tr>
<tr class="memdesc:acbeb087da86ee77a1d32cf9210a0f1e2 inherit pub_methods_classpcl_1_1cuda_1_1_sample_consensus"><td class="mdescLeft">&#160;</td><td class="mdescRight">Destructor for base SAC. <br /></td></tr>
<tr class="separator:acbeb087da86ee77a1d32cf9210a0f1e2 inherit pub_methods_classpcl_1_1cuda_1_1_sample_consensus"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a000379c2bf39b238d812ec5a55b281d2 inherit pub_methods_classpcl_1_1cuda_1_1_sample_consensus"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1cuda_1_1_sample_consensus.html#a000379c2bf39b238d812ec5a55b281d2">setDistanceThreshold</a> (float threshold)</td></tr>
<tr class="memdesc:a000379c2bf39b238d812ec5a55b281d2 inherit pub_methods_classpcl_1_1cuda_1_1_sample_consensus"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set the distance to model threshold.  <a href="classpcl_1_1cuda_1_1_sample_consensus.html#a000379c2bf39b238d812ec5a55b281d2">更多...</a><br /></td></tr>
<tr class="separator:a000379c2bf39b238d812ec5a55b281d2 inherit pub_methods_classpcl_1_1cuda_1_1_sample_consensus"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:aec2105e1e32c118dbfb2bbb3f55b5df8 inherit pub_methods_classpcl_1_1cuda_1_1_sample_consensus"><td class="memItemLeft" align="right" valign="top"><a id="aec2105e1e32c118dbfb2bbb3f55b5df8"></a>
float&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1cuda_1_1_sample_consensus.html#aec2105e1e32c118dbfb2bbb3f55b5df8">getDistanceThreshold</a> ()</td></tr>
<tr class="memdesc:aec2105e1e32c118dbfb2bbb3f55b5df8 inherit pub_methods_classpcl_1_1cuda_1_1_sample_consensus"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get the distance to model threshold, as set by the user. <br /></td></tr>
<tr class="separator:aec2105e1e32c118dbfb2bbb3f55b5df8 inherit pub_methods_classpcl_1_1cuda_1_1_sample_consensus"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:aa6ec12c07572b64574b6500e6d8a9dcb inherit pub_methods_classpcl_1_1cuda_1_1_sample_consensus"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1cuda_1_1_sample_consensus.html#aa6ec12c07572b64574b6500e6d8a9dcb">setMaxIterations</a> (int max_iterations)</td></tr>
<tr class="memdesc:aa6ec12c07572b64574b6500e6d8a9dcb inherit pub_methods_classpcl_1_1cuda_1_1_sample_consensus"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set the maximum number of iterations.  <a href="classpcl_1_1cuda_1_1_sample_consensus.html#aa6ec12c07572b64574b6500e6d8a9dcb">更多...</a><br /></td></tr>
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<tr class="memitem:a3d395cce84129159371a366b437b812a inherit pub_methods_classpcl_1_1cuda_1_1_sample_consensus"><td class="memItemLeft" align="right" valign="top"><a id="a3d395cce84129159371a366b437b812a"></a>
int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1cuda_1_1_sample_consensus.html#a3d395cce84129159371a366b437b812a">getMaxIterations</a> ()</td></tr>
<tr class="memdesc:a3d395cce84129159371a366b437b812a inherit pub_methods_classpcl_1_1cuda_1_1_sample_consensus"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get the maximum number of iterations, as set by the user. <br /></td></tr>
<tr class="separator:a3d395cce84129159371a366b437b812a inherit pub_methods_classpcl_1_1cuda_1_1_sample_consensus"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ae97e118b7ca3ab88bf0680f718f28366 inherit pub_methods_classpcl_1_1cuda_1_1_sample_consensus"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1cuda_1_1_sample_consensus.html#ae97e118b7ca3ab88bf0680f718f28366">setProbability</a> (float probability)</td></tr>
<tr class="memdesc:ae97e118b7ca3ab88bf0680f718f28366 inherit pub_methods_classpcl_1_1cuda_1_1_sample_consensus"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set the desired probability of choosing at least one sample free from outliers.  <a href="classpcl_1_1cuda_1_1_sample_consensus.html#ae97e118b7ca3ab88bf0680f718f28366">更多...</a><br /></td></tr>
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<tr class="memitem:ad4f5d9a11015deaa4042093cfc9bce3e inherit pub_methods_classpcl_1_1cuda_1_1_sample_consensus"><td class="memItemLeft" align="right" valign="top"><a id="ad4f5d9a11015deaa4042093cfc9bce3e"></a>
float&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1cuda_1_1_sample_consensus.html#ad4f5d9a11015deaa4042093cfc9bce3e">getProbability</a> ()</td></tr>
<tr class="memdesc:ad4f5d9a11015deaa4042093cfc9bce3e inherit pub_methods_classpcl_1_1cuda_1_1_sample_consensus"><td class="mdescLeft">&#160;</td><td class="mdescRight">Obtain the probability of choosing at least one sample free from outliers, as set by the user. <br /></td></tr>
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<tr class="memitem:a2b71a061a291e1100eb9249e8e3a427f inherit pub_methods_classpcl_1_1cuda_1_1_sample_consensus"><td class="memItemLeft" align="right" valign="top"><a id="a2b71a061a291e1100eb9249e8e3a427f"></a>
IndicesPtr&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1cuda_1_1_sample_consensus.html#a2b71a061a291e1100eb9249e8e3a427f">getInliers</a> ()</td></tr>
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IndicesPtr&#160;</td><td class="memItemRight" valign="bottom"><b>getInliersStencil</b> ()</td></tr>
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Private 类型</h2></td></tr>
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typedef <a class="el" href="classpcl_1_1cuda_1_1_sample_consensus_model.html">SampleConsensusModel</a>&lt; Storage &gt;::Ptr&#160;</td><td class="memItemRight" valign="bottom"><b>SampleConsensusModelPtr</b></td></tr>
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typedef <a class="el" href="classpcl_1_1cuda_1_1_sample_consensus_model.html">SampleConsensusModel</a>&lt; Storage &gt;::IndicesPtr&#160;</td><td class="memItemRight" valign="bottom"><b>IndicesPtr</b></td></tr>
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typedef <a class="el" href="classpcl_1_1cuda_1_1_sample_consensus_model.html">SampleConsensusModel</a>&lt; Storage &gt;::IndicesConstPtr&#160;</td><td class="memItemRight" valign="bottom"><b>IndicesConstPtr</b></td></tr>
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Private 属性</h2></td></tr>
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float&#160;</td><td class="memItemRight" valign="bottom"><b>min_coverage_percent_</b></td></tr>
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unsigned int&#160;</td><td class="memItemRight" valign="bottom"><b>max_batches_</b></td></tr>
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unsigned int&#160;</td><td class="memItemRight" valign="bottom"><b>iterations_per_batch_</b></td></tr>
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std::vector&lt; float3 &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1cuda_1_1_multi_random_sample_consensus.html#ab45b595401f5795286dd1a2e2d4358c9">all_model_centroids_</a></td></tr>
<tr class="memdesc:ab45b595401f5795286dd1a2e2d4358c9"><td class="mdescLeft">&#160;</td><td class="mdescRight">The vector of the centroids of our models computed directly from the models found. <br /></td></tr>
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std::vector&lt; float4 &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1cuda_1_1_multi_random_sample_consensus.html#a167354539f4e650dd2f3c5c60bbf505f">all_model_coefficients_</a></td></tr>
<tr class="memdesc:a167354539f4e650dd2f3c5c60bbf505f"><td class="mdescLeft">&#160;</td><td class="mdescRight">The vector of coefficients of our models computed directly from the models found. <br /></td></tr>
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std::vector&lt; IndicesPtr &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>all_inliers_</b></td></tr>
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std::vector&lt; int &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>all_inlier_counts_</b></td></tr>
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额外继承的成员函数</h2></td></tr>
<tr class="inherit_header pub_types_classpcl_1_1cuda_1_1_sample_consensus"><td colspan="2" onclick="javascript:toggleInherit('pub_types_classpcl_1_1cuda_1_1_sample_consensus')"><img src="closed.png" alt="-"/>&#160;Public 类型 继承自 <a class="el" href="classpcl_1_1cuda_1_1_sample_consensus.html">pcl::cuda::SampleConsensus&lt; Storage &gt;</a></td></tr>
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typedef boost::shared_ptr&lt; Coefficients &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>CoefficientsPtr</b></td></tr>
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typedef boost::shared_ptr&lt; const Coefficients &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>CoefficientsConstPtr</b></td></tr>
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typedef boost::shared_ptr&lt; <a class="el" href="classpcl_1_1cuda_1_1_sample_consensus.html">SampleConsensus</a> &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>Ptr</b></td></tr>
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typedef boost::shared_ptr&lt; const <a class="el" href="classpcl_1_1cuda_1_1_sample_consensus.html">SampleConsensus</a> &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>ConstPtr</b></td></tr>
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<tr class="inherit_header pro_attribs_classpcl_1_1cuda_1_1_sample_consensus"><td colspan="2" onclick="javascript:toggleInherit('pro_attribs_classpcl_1_1cuda_1_1_sample_consensus')"><img src="closed.png" alt="-"/>&#160;Protected 属性 继承自 <a class="el" href="classpcl_1_1cuda_1_1_sample_consensus.html">pcl::cuda::SampleConsensus&lt; Storage &gt;</a></td></tr>
<tr class="memitem:ae2d1f0f11bd8bbac7a6b128cd27b6ced inherit pro_attribs_classpcl_1_1cuda_1_1_sample_consensus"><td class="memItemLeft" align="right" valign="top"><a id="ae2d1f0f11bd8bbac7a6b128cd27b6ced"></a>
SampleConsensusModelPtr&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1cuda_1_1_sample_consensus.html#ae2d1f0f11bd8bbac7a6b128cd27b6ced">sac_model_</a></td></tr>
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<tr class="memitem:a446259f179f2cef7d929550db5450b2e inherit pro_attribs_classpcl_1_1cuda_1_1_sample_consensus"><td class="memItemLeft" align="right" valign="top"><a id="a446259f179f2cef7d929550db5450b2e"></a>
Indices&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1cuda_1_1_sample_consensus.html#a446259f179f2cef7d929550db5450b2e">model_</a></td></tr>
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<tr class="memitem:a69c386c8a4cf1d31ee5284491b644287 inherit pro_attribs_classpcl_1_1cuda_1_1_sample_consensus"><td class="memItemLeft" align="right" valign="top"><a id="a69c386c8a4cf1d31ee5284491b644287"></a>
IndicesPtr&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1cuda_1_1_sample_consensus.html#a69c386c8a4cf1d31ee5284491b644287">inliers_</a></td></tr>
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IndicesPtr&#160;</td><td class="memItemRight" valign="bottom"><b>inliers_stencil_</b></td></tr>
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Coefficients&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1cuda_1_1_sample_consensus.html#a642fb690c064d7f06c917f3bc713995f">model_coefficients_</a></td></tr>
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float&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1cuda_1_1_sample_consensus.html#a4615fb95aea920a5f167f1db3a9dc2a8">probability_</a></td></tr>
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int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1cuda_1_1_sample_consensus.html#ae525abb9ec82e5e2b0ee473f05ba91fa">iterations_</a></td></tr>
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<tr class="memitem:a49491a73c6f6e408ee41b55884d2cacd inherit pro_attribs_classpcl_1_1cuda_1_1_sample_consensus"><td class="memItemLeft" align="right" valign="top"><a id="a49491a73c6f6e408ee41b55884d2cacd"></a>
float&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1cuda_1_1_sample_consensus.html#a49491a73c6f6e408ee41b55884d2cacd">threshold_</a></td></tr>
<tr class="memdesc:a49491a73c6f6e408ee41b55884d2cacd inherit pro_attribs_classpcl_1_1cuda_1_1_sample_consensus"><td class="mdescLeft">&#160;</td><td class="mdescRight">Distance to model threshold. <br /></td></tr>
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int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1cuda_1_1_sample_consensus.html#a56f971cd1025e8bcaa1c6da13080dca2">max_iterations_</a></td></tr>
<tr class="memdesc:a56f971cd1025e8bcaa1c6da13080dca2 inherit pro_attribs_classpcl_1_1cuda_1_1_sample_consensus"><td class="mdescLeft">&#160;</td><td class="mdescRight">Maximum number of iterations before giving up. <br /></td></tr>
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<a name="details" id="details"></a><h2 class="groupheader">详细描述</h2>
<div class="textblock"><h3>template&lt;template&lt; typename &gt; class Storage&gt;<br />
class pcl::cuda::MultiRandomSampleConsensus&lt; Storage &gt;</h3>

<p><b><a class="el" href="classpcl_1_1cuda_1_1_random_sample_consensus.html" title="RandomSampleConsensus represents an implementation of the RANSAC (RAndom SAmple Consensus) algorithm,...">RandomSampleConsensus</a></b> represents an implementation of the RANSAC (RAndom SAmple Consensus) algorithm, as described in: "Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography", Martin A. Fischler and Robert C. Bolles, Comm. Of the ACM 24: 381–395, June 1981. </p>
<dl class="section author"><dt>作者</dt><dd>Radu Bogdan Rusu </dd></dl>
</div><h2 class="groupheader">构造及析构函数说明</h2>
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<h2 class="memtitle"><span class="permalink"><a href="#af3a795dc44e988143c2bedb97f864938">&#9670;&nbsp;</a></span>MultiRandomSampleConsensus() <span class="overload">[1/2]</span></h2>

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          <td>(</td>
          <td class="paramtype">const SampleConsensusModelPtr &amp;&#160;</td>
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<p>RANSAC (RAndom SAmple Consensus) main constructor </p>
<dl class="params"><dt>参数</dt><dd>
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    <tr><td class="paramname">model</td><td>a Sample Consensus model </td></tr>
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<div class="fragment"><div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;                                                                          : </div>
<div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160;          SampleConsensus&lt;Storage&gt; (model),</div>
<div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160;          min_coverage_percent_ (0.9),</div>
<div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;          max_batches_ (5),</div>
<div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160;          iterations_per_batch_ (1000)</div>
<div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160;        {</div>
<div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;          <span class="comment">// Maximum number of trials before we give up.</span></div>
<div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;          <a class="code" href="classpcl_1_1cuda_1_1_sample_consensus.html#a56f971cd1025e8bcaa1c6da13080dca2">max_iterations_</a> = 10000;</div>
<div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;        }</div>
<div class="ttc" id="aclasspcl_1_1cuda_1_1_sample_consensus_html_a56f971cd1025e8bcaa1c6da13080dca2"><div class="ttname"><a href="classpcl_1_1cuda_1_1_sample_consensus.html#a56f971cd1025e8bcaa1c6da13080dca2">pcl::cuda::SampleConsensus::max_iterations_</a></div><div class="ttdeci">int max_iterations_</div><div class="ttdoc">Maximum number of iterations before giving up.</div><div class="ttdef"><b>Definition:</b> sac.h:196</div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#a2d30c49d773e4ac88bf50d5019edd09e">&#9670;&nbsp;</a></span>MultiRandomSampleConsensus() <span class="overload">[2/2]</span></h2>

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          <td>(</td>
          <td class="paramtype">const SampleConsensusModelPtr &amp;&#160;</td>
          <td class="paramname"><em>model</em>, </td>
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          <td class="paramtype">double&#160;</td>
          <td class="paramname"><em>threshold</em>&#160;</td>
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<p>RANSAC (RAndom SAmple Consensus) main constructor </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramname">model</td><td>a Sample Consensus model </td></tr>
    <tr><td class="paramname">threshold</td><td>distance to model threshold </td></tr>
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  </dd>
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<div class="fragment"><div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160;                                                                                            : </div>
<div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;          SampleConsensus&lt;Storage&gt; (model, threshold)</div>
<div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;        {</div>
<div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;          <span class="comment">// Maximum number of trials before we give up.</span></div>
<div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;          <a class="code" href="classpcl_1_1cuda_1_1_sample_consensus.html#a56f971cd1025e8bcaa1c6da13080dca2">max_iterations_</a> = 10000;</div>
<div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;        }</div>
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<h2 class="groupheader">成员函数说明</h2>
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<h2 class="memtitle"><span class="permalink"><a href="#ae85ed7f021732575132f9a4b30a73eb5">&#9670;&nbsp;</a></span>computeModel()</h2>

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          <td class="memname">bool <a class="el" href="classpcl_1_1cuda_1_1_multi_random_sample_consensus.html">pcl::cuda::MultiRandomSampleConsensus</a>&lt; Storage &gt;::computeModel </td>
          <td>(</td>
          <td class="paramtype">int&#160;</td>
          <td class="paramname"><em>debug_verbosity_level</em> = <code>0</code></td><td>)</td>
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<p>Compute the actual model and find the inliers </p>
<dl class="params"><dt>参数</dt><dd>
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    <tr><td class="paramname">debug_verbosity_level</td><td>enable/disable on-screen debug information and set the verbosity level </td></tr>
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<p>实现了 <a class="el" href="classpcl_1_1cuda_1_1_sample_consensus.html#a9fcca984265ea67226bf94ef1537a173">pcl::cuda::SampleConsensus&lt; Storage &gt;</a>.</p>

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<li>cuda/sample_consensus/include/pcl/cuda/sample_consensus/<a class="el" href="multi__ransac_8h_source.html">multi_ransac.h</a></li>
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